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Complicated nano- and atomic-scale processes with sub-angstrom spatial resolution and millisecond time resolution visualized by in situ transmission electron microscopy (TEM) are often highly dynamical and time consuming to analyze and interpret. Here, we report how variational autoencoders (VAEs) can provide an artificial intelligence's interpretation of high-resolution in situ TEM data by condensing and deconvoluting complicated atomic-scale dynamics into a latent space with reduced dimensionality. We designed a VAE model with high latent dimensions capable of deconvoluting information from complex high-resolution TEM data. We demonstrate how this model, with high latent dimensions trained on atomically resolved TEM images of lead sulfide (PbS) nanocrystals, is able to capture movements and perturbations of periodic lattices in both simulated and real in situ TEM data. The VAE model shows the capability of detecting and deconvoluting dynamical nanoscale physical processes, such as the rotation of crystal lattices and intra-particle ripening during the annealing of semiconductor nanocrystals.

BackgroundCompared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health.ObjectivesThis study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California.MethodsSES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status.ResultsThe accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): -3.02, -2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities.ConclusionStreet view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

Abstract Background Though bictegravir/emtricitabine/tenofovir (BIC/FTC/TAF) have been regulatory approved and included in the National Reimbursement Drug List in China, due to the affordability concern, generic version of efavirenz + lamivudine + tenofovir (EFV + 3TC + TDF) is still recommended as the first-line therapy in the clinical guideline and widely used in clinical practice. The aim of the study is to assess the persistence with first-line BIC/TAF/TAF and EFV + 3TC + TDF in newly treated HIV-1 patients in the real-world setting in Hunan Province in China. Methods A retrospective analysis of the medical records of HIV patients initiating first-line antiretroviral therapy in the First Hospital of Changsha in January 1st, 2021-July 31st, 2022 was conducted. Persistence was assessed as the number of days on the therapy from the index until treatment discontinuation or end of data availability. Kaplan-Meier Curves and Cox Proportional Hazard models were used to evaluate the discontinuation rates. Subgroup analysis was performed excluding BIC/FTC/TAF patients with treatment discontinuation due to economic reason, and EFV + 3TC + TDF patients with a viral load > 500,000 copies/mL. Results A total of 310 eligible patients were included in the study, with 244 and 66 patients in the BIC/FTC/TAF group and EFV + 3TC + TDF group, respectively. Compared with EFV + 3TC + TDF patients, BIC/FTC/TAF patients were older, more living in the capital city currently, and had significantly higher total cholesterol and low-density level (all p 500,000 copies/mL, the analysis showed similar results (HR = 10.1, 95% CI = 1.2–84.1). 79.4% of the EFV + 3TC + TDF patients discontinued treatment due to clinical reasons, while 83.3% of the BIC/FTC/TAF patients discontinued treatment due to economic reasons. Conclusions Compared with BIC/FTC/TAF, EFV + TDF + 3TC patients were significantly more likely to discontinue the first-line treatment in Hunan Province in China.

Colloidal superlattices are fascinating materials made of ordered nanocrystals, yet they are rarely called "atomically precise". That is unsurprising, given how challenging it is to quantify the degree of structural order in these materials. However, once that order crosses a certain threshold, the constructive interference of X-rays diffracted by the nanocrystals dominates the diffraction pattern, offering a wealth of structural information. By treating nanocrystals as scattering sources forming a self-probing interferometer, we developed a multilayer diffraction method that enabled the accurate determination of the nanocrystal size, interparticle spacing, and their fluctuations for samples of self-assembled CsPbBr3 and PbS nanomaterials. The multilayer diffraction method requires only a laboratory-grade diffractometer and an open-source fitting algorithm for data analysis. The average nanocrystal displacement of 0.33 to 1.43 Å in the studied superlattices provides a figure of merit for their structural perfection and approaches the atomic displacement parameters found in traditional crystals.

Increasing access to undergraduate research is critical in efforts to retain students pursuing careers in STEM. Alternatives to traditional research positions, such as course based undergraduate research experiences (CUREs), have played important roles in engaging more undergraduates in research. However, these opportunities are only a subset of possible nontraditional research experiences which can enrich the undergraduate experience. In this study, we critically examined and improved the research-group-led undergraduate research program (GURP) to better meet the needs of undergraduates seeking research. Specifically, we investigated if the program was successful with a greater diversity of data sets, if assessments of learning were consistent across different data sets and teaching environments, and if the program promoted student engagement in research. We have found that this model is scalable, robust, and adaptable to different implementations while producing consistent and positive learning outcomes for students. Especially remarkable are students' increased self-identification as scientists and statistically significant gains in self-perceived competency across multiple domains of knowledge. This program model has shown promising results as a partially and fully online research experience for undergraduates and has benefited program alumni in their research careers. To assist research groups starting similar programs, we have created public data sets and instructional resources. We believe that GURP programs can work in a variety of situations and hope that they can become a tool to increase interest and build communities for young researchers.

The synthesis quality of artificial inorganic nanocrystals is most often assessed by transmission electron microscopy (TEM) for which high-throughput advances have dramatically increased both the quantity and information richness of metal nanoparticle (mNP) characterization. Existing automated data analysis algorithms of TEM mNP images generally adopt a supervised approach, requiring a significant effort in human preparation of labeled data that reduces objectivity, efficiency, and generalizability. We have developed an unsupervised algorithm AutoDetect-mNP for automated analysis of TEM images that objectively extracts morphological information on convex mNPs from TEM images based on their shape attributes, requiring little to no human input in the process. The performance of AutoDetect-mNP is tested on two data sets of bright field TEM images of Au nanoparticles with different shapes and further extended to palladium nanocubes and cadmium selenide quantum dots, demonstrating that the algorithm is quantitatively reliable and can thus serve as a generalizable measure of the morphology distributions of any mNP synthesis. The AutoDetect-mNP algorithm will aid in future developments of high-throughput characterization of mNPs and the future advent of time-resolved TEM studies that can investigate reaction mechanisms of mNP synthesis and reactivity.

The fluid distribution is considerably influenced by the reservoir homogeneity. This study analyzes carbonate reservoir heterogeneity and hydrocarbon accumulation processes in the Khasib reservoirs in AB oilfield in the Middle East to discuss factors controlling the distribution of fluid and the effects of this distribution on the productivity of single wells. The Khasib reservoirs in the AB oilfield show uniform vertical distributions, with a large difference in the longitudinal pore throats, and the upper part of the reservoir is better than the lower one. Because the hydrocarbon accumulation is multiphasic, the heterogeneity of crude oil is based on whether traps are formed in the early or late stage of source rock formation during accumulation. Moreover, the longitudinal physical differences in crude oil properties are mainly attributed to the longitudinal differences in the pore throats. The difference in the pore throat and physical properties of crude oil affords differences in the productivity of horizontal wells in different subzones.

Recently, two-dimensional magnetic materials (2DMMs) have become a focused research direction in a broad range of two-dimensional materials, due to their underlying significance in fundamental research, as well as in technologically relevant applications for future spintronics, magnonics, quantum information and data storage. The rich toolbox of 2DMMs and their diverse tunability have enabled the unprecedented research concerning the two-dimensional magnetic order down to single atomic layer of materials, much beyond the classical thin film magnetism, showing an extremely promising avenue towards electronics, magneto-optics and photonics. Among various degrees of freedoms, the interaction between spin and phonon (i.e., quanta of lattice vibration), hence the so-called spin-phonon coupling, serves as an important tuning knob to explore the two-dimensional magnetism, creates new types of quasiparticles and controls the magnetic order. This review overviews the latest developments in spin-phonon coupling investigations in 2DMMs. Various techniques utilizing spin-phonon coupling to investigate two-dimensional magnetism are discussed. Recent progress in tuning two-dimensional magnetic order based on spin-phonon coupling is also summarized, with a focus to understand the new functionalities. Furthermore, device developments and concepts based on spin-phonon coupling are briefly discussed. This review will provide our perspectives on the existing challenges and future directions in spin-phonon coupling research in 2DMMs and their functional devices.

During the joint extraction of entity and relationship from the operational management data of hydraulic engineering, complex sentences containing multiple triplets and overlapping entity relations often arise. However, traditional joint extraction models suffer from a single-feature representation approach, which hampers the effectiveness of entity relation extraction in complex sentences within hydraulic engineering datasets. To address this issue, this study proposes a multi-feature joint entity relation extraction method based on global context mechanism and graph convolutional neural networks. This method builds upon the Bidirectional Encoder Representations from Transformers (BERT) pre-trained model and utilizes a bidirectional gated recurrent unit (BiGRU) and global context mechanism (GCM) to supplement the contextual and global features of sentences. Subsequently, a graph convolutional network (GCN) based on syntactic dependencies is employed to learn inter-word dependency features, enhancing the model's knowledge representation capabilities for complex sentences. Experimental results demonstrate the effectiveness of the proposed model in the joint extraction task on hydraulic engineering datasets. The precision, recall, and F1-score are 86.5%, 84.1%, and 85.3%, respectively, all outperforming the baseline model. [ABSTRACT FROM AUTHOR]